DDA-SKF: Predicting Drug–Disease Associations Using Similarity Kernel Fusion

作者：Chu-Qiao Gao, Yuan-Ke Zhou, Xiaohong Xin, Hui Min, Pu-Feng Du · 发表于：Frontiers in Pharmacology · 年份：2022 · DOI：10.3389/fphar.2021.784171 · 被引用次数：22 · 研究领域：Computational Drug Discovery Methods、Pharmacogenetics and Drug Metabolism、Bioinformatics and Genomic Networks

Drug repositioning provides a promising and efficient strategy to discover potential associations between drugs and diseases. Many systematic computational drug-repositioning methods have been introduced, which are based on various similarities of drugs and diseases. In this work, we proposed a new computational model, DDA-SKF (drug-disease associations prediction using similarity kernels fusion), which can predict novel drug indications by utilizing similarity kernel fusion (SKF) and Laplacian regularized least squares (LapRLS) algorithms. DDA-SKF integrated multiple similarities of drugs and diseases. The prediction performances of DDA-SKF are better, or at least comparable, to all state-of-the-art methods. The DDA-SKF can work without sufficient similarity information between drug indications. This allows us to predict new purpose for orphan drugs. The source code and benchmarking datasets are deposited in a GitHub repository (https://github.com/GCQ2119216031/DDA-SKF).